The first tomograms were obtained from fixed samples at room temperature, and opened a dramatic new window into the ultrastructure of cells [1]. They made it abundantly clear that the high­resolution, 3­D information present in tomograms would be essential for understanding the complex spatial relationships of organelles, microtubules, vesicles, ribosomes, and other large structures within cells. Efforts were then launched to perform “serial­section, montage” tomography, where beam and image shifts are used to record a tiled montage of large (a few microns square) areas of a series of sequential sections, and then all the resultant tomograms are stitched together to produce a 3­D reconstruction of a substantial fraction of a cellular volume [3]. This will eventually make it possible to reconstruct representative human cells in their entirety by serial section montage tomography, in both healthy and diseased conditions, at nanoscopic scale. Real high-­throughput technology is crucial to achieve this goal since each single cell montage requires thousands of tomograms to be acquired and processed.

One of the main challenges common to all this work is the large number of tomograms that need to be obtained in order to understand cellular processes and structures. High-throughput data processing for Electron Tomography is not only a nice tool to have, but it is a vital one to understand cells better. One approach is to image mutants where the subject of interest is over­expressed, deleted, depleted, or otherwise altered, and then compare them closely to wild­type cells to see which ultrastructures have become larger, smaller, or otherwise changed [2]. However, this can require hundreds of tomograms, just to identify one structure of interestin one organism. Tomography also has the potential to capture dynamic events [4] which only occur within cells, like the assembly or segregation of organelles. Unfortunately, many such events are short­lived, so many tomograms have to be recorded to capture a statistically relevant number of examples.

Figure 1: Image patch of the same cluster of gold beads in a cryo-electron micrographs at different tilt angles. The tilt series shows two main challenges during the alignment process: first, beads clump together making it hard to keep correct identity along teh tilt series. Second, contrast decreseases significantly with tilt angle, making the detection problem harder. Gold bead dimater is 10nm, which is approximately 8 pixels in each image.

Thus, it is abundantly clear that hundreds of thousands and perhaps many millions of tomograms will be needed in coming years. Towards this end, complex software packages such as IMOD, bSoft, Leginon, SerialEM or TOM toolbox, have recently been developed to automate different aspects of the the ET pipeline presented below to achieve the necessary high­-throughput ( Click here for a complete list of available software ). In particular, my research has focused on fully automatic alignment of tomograms with fiducial markers (Fig.1) , which is one of the bottle necks in the current pipeline. Using probabilistic models, an in particular Markov Random Fields, we are able to track fiducial markers across tilt-series even under veru low SNR conditions. This research efforts lead to the release of RAPTOR, an open-source software quickly adopted by the cryo-EM community which has helped increasing the throughput in many labs. RAPTOR has been tested in thousands of different datasets (cryo-EM, plastic, thick sections…) and different geometries and has given very good results (Fig. 2). So far, the best results have been obtained in cryo. We continue working to improve results in challenging situations such as large field images with hundreds of fiducial markers and non-linear optical distortions.

Figure 2: Final tomogram resolution comparison for two different datasets between guided semi-automatic reconstruction by an expert user and fully automatic alignment by RAPTOR. The algorithm developed in this porject can be incorporated in a high-trhoughput pipeline to increase the number of tomograms analyzed in different projects. (From [5]).

[3] Marsh, B. J., D. N. Mastronarde, Buttle, K.F., Howell, K.E. and McIntosh, J.R. (2001). "Organellar relationships in the Golgi region of the pancreatic beta cell line, HIT­T15, visualized by high resolution electron tomography." Proceedings of the National Academy of Sciences of the United States of America 98(5): 2399­2406.